FedCARR: Contribution-Aware Retention and Rollback Optimization for Federated Unlearning

Federated unlearning (FU) aims to remove the influence of clients requesting data deletion from a collaboratively trained model, but existing approaches primarily focus on the operational execution of unlearning while overlooking the economic decisions surrounding batched deletion requests. In practical federated learning systems, a server may have a limited compensation budget and must jointly determine which clients to temporarily retain and which historical model state to use for subsequent unlearning. We propose FedCARR, a two-stage mechanism for contribution-aware batched client-level FU. In the first stage, the server assigns contribution-weighted compensation caps and determines individually rational retention decisions under a hard budget constraint. These decisions and associated payments are then fixed as binding commitments. In the second stage, given the resulting set of clients to be forgotten, the server selects an executable rollback state from loadable or reconstructible historical checkpoints by jointly considering retained model utility, compensation expenditure, and delayed-deletion privacy costs. We theoretically establish individual rationality, retention-decision stability, the existence and optimality of feasible rollback decisions, and the existence of an optimal solution to the formulated problem. Extensive experiments on CIFAR-10, CIFAR-100, and Fashion-MNIST evaluate the tradeoffs among model utility, compensation cost, deletion delay, and rollback requirements, demonstrating the effectiveness of the proposed mechanism for budget-constrained FU.